The challenging task of handwriting style synthesis requires capturing the individuality and diversity of human handwriting. The majority of currently available methods use either a generative adversarial network (GAN) or a recurrent neural network (RNN) to generate new handwriting styles. This is why these techniques frequently fall short of producing diverse and realistic text pictures, particularly for terms that are not commonly used. To resolve that, this research proposes a novel deep learning model that consists of a style encoder and a text generator to synthesize different handwriting styles. This network excels in generating conditional text by extracting style vectors from a series of style images. The model performs admirably on a range of handwriting synthesis tasks, including the production of text that is out-of-vocabulary. It works more effectively than previous approaches by displaying lower values on key Generative Adversarial Network evaluation metrics, such Geometric Score (GS) (3.21 × 10⁻⁵) and Fréchet Inception Distance (FID) (8.75), as well as text recognition metrics, like Character Error Rate (CER) and Word Error Rate (WER). A thorough component analysis revealed the steady improvement in image production quality, highlighting the importance of specific handwriting styles. Applicable fields include digital forensics, creative writing, and document security.

A new era of data access and management has begun with the use of cloud computing in the healthcare industry. Despite the efficiency and scalability that the cloud provides, the security of private patient data is still a major concern. Encryption, network security, and adherence to data protection laws are key to ensuring the confidentiality and integrity of healthcare data in the cloud. The computational overhead of encryption technologies could lead to delays in data access and processing rates. To address these challenges, we introduced the Enhanced Parallel Multi-Key Encryption Algorithm (EPM-KEA), aiming to bolster healthcare data security and facilitate the secure storage of critical patient records in the cloud. The data was gathered from two categories Authorization for Hospital Admission (AIH) and Authorization for High Complexity Operations. We use Z-score normalization for preprocessing. The primary goal of implementing encryption techniques is to secure and store massive amounts of data on the cloud. It is feasible that cloud storage alternatives for protecting healthcare data will become more widely available if security issues can be successfully fixed. As a result of our analysis using specific parameters including Execution time (42%), Encryption time (45%), Decryption time (40%), Security level (97%), and Energy consumption (53%), the system demonstrated favorable performance when compared to the traditional method. This suggests that by addressing these security concerns, there is the potential for broader accessibility to cloud storage solutions for safeguarding healthcare data.

The exponential growth of the Internet of Things (IoT) has revolutionized various domains such as healthcare, smart cities, and agriculture, generating vast volumes of data that require secure processing and storage in cloud environments. However, reliance on cloud infrastructure raises critical security challenges, particularly regarding data integrity. While existing cryptographic methods provide robust integrity verification, they impose significant computational and energy overheads on resource-constrained IoT devices, limiting their applicability in large-scale, real-time scenarios. To address these challenges, we propose the Cognitive-Based Integrity Verification Model (C-BIVM), which leverages Belief-Desire-Intention (BDI) cognitive intelligence and algebraic signatures to enable lightweight, efficient, and scalable data integrity verification. The model incorporates batch auditing, reducing resource consumption in large-scale IoT environments by approximately 35%, while achieving an accuracy of over 99.2% in detecting data corruption. C-BIVM dynamically adapts integrity checks based on real-time conditions, optimizing resource utilization by minimizing redundant operations by more than 30%. Furthermore, blind verification techniques safeguard sensitive IoT data, ensuring privacy compliance by preventing unauthorized access during integrity checks. Extensive experimental evaluations demonstrate that C-BIVM reduces computation time for integrity checks by up to 40% compared to traditional bilinear pairing-based methods, making it particularly suitable for IoT-driven applications in smart cities, healthcare, and beyond. These results underscore the effectiveness of C-BIVM in delivering a secure, scalable, and resource-efficient solution tailored to the evolving needs of IoT ecosystems.